Gas chemosensors based on metal oxides are made of a sensitive layer whose resistivity is measured. To carry out measurements, setting the temperature of said layer in a determined range that usually depends on the particular characteristics of the layer (normally in the 150° C.-500° C. range) is needed. To that end, the sensor normally includes a heating element located close to the sensitive layer that is used to set the temperature thereof during the sensor operation.
The common use mode of these sensors consists in setting a determined temperature on the sensor (with the heating resistor) and continuously monitoring the value of the chemical resistivity. The dynamics of the sensor system is then determined by the kinetics of the chemical reactions themselves that take place in the sensitive layer based on the gases present. This kinetics is extremely slow (10-20 minutes), [1]. The concentrations of absorbed species in the layer based on the gases that the sensor contacts generate variations on the value of chemical resistivity. These are the variations whose measurement is intended.
It is important to highlight that the measurement is carried out at a constant temperature, since the resistivity of the sensitive layer also varies based on its own temperature. The sensitive layer is made of metal oxides and, therefore, these are semiconductive. This causes the dependence of the chemical resistivity on temperature to be very high, and for it to have, in addition, very fast kinetics.
In the state of the art, numerous semiconductive metal oxides are known, such as, without limitation, NiO, CuO, Cr2O3, Co3O4, Mn3O4, ZnO, SnO2, TiO2, WO3, In2O3, or Fe2O3. In the state of the art, it is known that there exist two types of semiconductive metal oxides, known as type n semiconductors and type p semiconductors, that show differences regarding their conductivity, which, in turn, influences the electrical properties of the sensor that comprises a sensitive layer formed by some of these oxides.
If the sensitive layer is formed by a type n metal oxide such as, without limitation, ZnO, SnO2, TiO2, WO3, In2O3, or Fe2O3, in the presence of air, and therefore of oxygen, oxygen molecules are absorbed into the surface that become ionized, giving rise to different oxygen species (e.g. O2, O−, or O2−). This attracts the electrons close to the surface of the sensitive layer, giving rise to the formation of a core-shell configuration in which the core shows properties typical of a semiconductor, whereas the shell shows a resistive electrical dual layer.
In contrast, if the sensitive layer is formed by a type p metal oxide such as, without limitation, NiO, CuO, Cr2O3, Co3O4, or Mn3O4, in the presence of air, and therefore of oxygen, a core-shell configuration is formed in which the shell shows semiconductive properties through an accumulation of voids, whereas que the core behaves similarly to an insulation.
Chemical resistivity depends on:    A) The instant temperature. The dependence is very high and shows very fast dynamics (temperature changes that generate changes in the resistivity in periods smaller than 1 ms). It does not provide any type of information relating to the gases whose measurement is intended, the latter possibly being an interfering signal.    B) The concentrations of absorbed species within the layer based on the gases to which they have been exposed (related with chemosorption and physisorption phenomena). This dependence has very slow kinetics. However, it is the one that provides information about changes in the concentrations of the gases that the sensor is in contact with.
Since the sensor is normally used at a constant temperature, the first dependence does not represent a problem, completely dominating the second mechanism, which is the desired one.
The problem, however, is that the response times if the sensor is used this way are relatively very high, and the recovery time, in particular, is often extremely long.
On the other hand, other more advanced methods exist for the measurement of gases taking advantage of their own transient response. The response of chemical transducers to external stimuli is produced both in permanent regime and in the shape of a transient response [2-5]. To infer the temporary evolution of the concentrations of gases to be measured, a measurement protocol is usually followed in which the sensors are exposed to reference air and to a gas sample of which information is desired. Based on the study of the transient among signals, a calibration model is obtained that allows improving the response time of the sensors [6-7]. One of the disadvantages of this approach is the complexity in the treatment of the samples, as well as the need for some control over the exposure to gases. Extensive work has been done to obtain dynamic models of sensors [8-11], recognizing the need to work with non-linear models in general. This lack of linearity is a problem when it comes to generating prediction algorithms of the temporal evolution of the gas concentrations. In this regard, some studies have been oriented to improving the response time of the chemo-resistive transducers based on a post-processing of the raw data provided by the transducers. In this context, studies that employ neural networks [12-13] or “support vector machines” deserve particular mention [14]. Another approach has consisted in treating the system as a linear system changing in time, consisting in switching between linear models, depending on whether it is estimated that the sensor is in a ramp-up or recovery phase [15], using Kalman filtering to obtain an estimate of the concentration to be measured.
In any case, all of these approaches assume a very high computational load, they are sensitive to noise and they have not been able to obtain a substantial improvement in the response time of the system.